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Abstract #3521

DISPUTE - Dixon Inspired Spectral Prediction UTE

Stefan Sommer1,2, Cui Lumeng3, and Daniel Nanz1
1Swiss Center for Musculoskeletal Imaging (SCMI), Balgrist Campus, Zurich, Switzerland, Zurich, Switzerland, 2Advanced Clinical Imaging Technology (ACIT), Siemens Healthineers International AG, Zurich, Switzerland, Zurich, Switzerland, 3Siemens Healthcare Limited, Burnaby, British Columbia, Canada, Burnaby, BC, Canada

Synopsis

Keywords: Fat & Fat/Water Separation, Fat and Fat/Water Separation, AI/ML Image Reconstruction, Contrast Mechanisms, Data Acquisition, Machine Learning/Artificial Intelligence, UTE, Dixon, Water Excitation, Fat Excitation, Ultra-high field, 7T

Motivation: At high-field strength, chemical-shift artifacts significantly impair image quality, especially for 3D radial sequences such as UTE or PETRA.

Goal(s): To train deep-learning models to generate water-only and fat-only images from conventional single-echo UTE data, facilitating Dixon-like fat/water separation without additional acquisition time.

Approach: We acquired UTE, water-excitation (WE), and fat-excitation (FE) UTE images of the knee and trained supervised models to predict water, fat, and chemical-shift-corrected images from the conventional UTE images.

Results: Predicted water and fat images reflect anatomical details observed in the corresponding ground-truth images and a contrast was achieved free of chemical shift.

Impact: We present a novel deep-learning Dixon-like fat/water separation and chemical-shift suppression for ultra-short echo time (UTE) MRI. Our method predicts water-only and fat-only images from conventional 7T UTE data, demonstrating a promising approach for high-field imaging applications.

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